{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 9: Portfolio Optimization with Risk Factors and Principal Components Regression (PCR)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  30 of 30 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Tickers of factors\n",
    "\n",
    "factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']\n",
    "factors.sort()\n",
    "\n",
    "tickers = assets + factors\n",
    "tickers.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(tickers, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.4735%</td>\n",
       "      <td>0.2672%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6780%</td>\n",
       "      <td>0.1635%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.5267%</td>\n",
       "      <td>-1.1914%</td>\n",
       "      <td>-0.5380%</td>\n",
       "      <td>-0.6253%</td>\n",
       "      <td>-1.8277%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.2293%</td>\n",
       "      <td>-2.3798%</td>\n",
       "      <td>-1.7181%</td>\n",
       "      <td>-1.6215%</td>\n",
       "      <td>-2.1609%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.9548%</td>\n",
       "      <td>-1.1377%</td>\n",
       "      <td>-1.1978%</td>\n",
       "      <td>-1.0086%</td>\n",
       "      <td>-1.0873%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.6043%</td>\n",
       "      <td>0.1480%</td>\n",
       "      <td>-0.5898%</td>\n",
       "      <td>0.1491%</td>\n",
       "      <td>-0.6183%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MTUM     QUAL     SIZE     USMV     VLUE\n",
       "Date                                                   \n",
       "2016-01-05  0.4735%  0.2672%  0.0000%  0.6780%  0.1635%\n",
       "2016-01-06 -0.5267% -1.1914% -0.5380% -0.6253% -1.8277%\n",
       "2016-01-07 -2.2293% -2.3798% -1.7181% -1.6215% -2.1609%\n",
       "2016-01-08 -0.9548% -1.1377% -1.1978% -1.0086% -1.0873%\n",
       "2016-01-11  0.6043%  0.1480% -0.5898%  0.1491% -0.6183%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "X = data[factors].pct_change().dropna()\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios with PCR\n",
    "\n",
    "### 2.1 Estimating the loadings matrix with PCR.\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix using PCR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_071e3_row0_col0, #T_071e3_row0_col1, #T_071e3_row14_col3, #T_071e3_row15_col2, #T_071e3_row15_col5, #T_071e3_row24_col4 {\n",
       "  background-color: #a50026;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row0_col2 {\n",
       "  background-color: #0d8044;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row0_col3, #T_071e3_row0_col5, #T_071e3_row10_col0, #T_071e3_row14_col1, #T_071e3_row14_col2, #T_071e3_row15_col4 {\n",
       "  background-color: #006837;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row0_col4 {\n",
       "  background-color: #af0926;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row1_col0, #T_071e3_row14_col0 {\n",
       "  background-color: #0f8446;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row1_col1 {\n",
       "  background-color: #eef8a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row1_col2, #T_071e3_row2_col0 {\n",
       "  background-color: #5ab760;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row1_col3 {\n",
       "  background-color: #feec9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row1_col4 {\n",
       "  background-color: #fee491;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row1_col5, #T_071e3_row4_col2, #T_071e3_row17_col3 {\n",
       "  background-color: #e3f399;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row2_col1, #T_071e3_row22_col3 {\n",
       "  background-color: #d9ef8b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row2_col2, #T_071e3_row19_col0 {\n",
       "  background-color: #daf08d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row2_col3 {\n",
       "  background-color: #feca79;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row2_col4 {\n",
       "  background-color: #cfeb85;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row2_col5 {\n",
       "  background-color: #fec877;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row3_col0 {\n",
       "  background-color: #f88950;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row3_col1, #T_071e3_row7_col1, #T_071e3_row11_col0 {\n",
       "  background-color: #feefa3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row3_col2, #T_071e3_row18_col4 {\n",
       "  background-color: #f1f9ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row3_col3 {\n",
       "  background-color: #fff8b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row3_col4 {\n",
       "  background-color: #eff8aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row3_col5, #T_071e3_row12_col1 {\n",
       "  background-color: #fee593;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row4_col0, #T_071e3_row5_col0, #T_071e3_row20_col2 {\n",
       "  background-color: #cbe982;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row4_col1 {\n",
       "  background-color: #fff0a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row4_col3, #T_071e3_row14_col4 {\n",
       "  background-color: #fffbb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row4_col4, #T_071e3_row20_col5 {\n",
       "  background-color: #f2faae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row4_col5, #T_071e3_row10_col1 {\n",
       "  background-color: #feeb9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row5_col1, #T_071e3_row13_col2 {\n",
       "  background-color: #f99153;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row5_col2 {\n",
       "  background-color: #e75337;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row5_col3, #T_071e3_row18_col3 {\n",
       "  background-color: #b5df74;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row5_col4 {\n",
       "  background-color: #4bb05c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row5_col5 {\n",
       "  background-color: #e95538;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row6_col0 {\n",
       "  background-color: #fba05b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row6_col1 {\n",
       "  background-color: #f99355;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row6_col2 {\n",
       "  background-color: #d62f27;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row6_col3, #T_071e3_row16_col3 {\n",
       "  background-color: #c1e57b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row6_col4 {\n",
       "  background-color: #39a758;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row6_col5 {\n",
       "  background-color: #dd3d2d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row7_col0 {\n",
       "  background-color: #097940;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row7_col2 {\n",
       "  background-color: #3ca959;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row7_col3, #T_071e3_row15_col0 {\n",
       "  background-color: #f7fcb4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row7_col4, #T_071e3_row23_col1 {\n",
       "  background-color: #fdad60;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row7_col5 {\n",
       "  background-color: #b1de71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row8_col0 {\n",
       "  background-color: #9dd569;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row8_col1, #T_071e3_row18_col2 {\n",
       "  background-color: #f8fcb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row8_col2 {\n",
       "  background-color: #07753e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row8_col3 {\n",
       "  background-color: #fffab6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row8_col4 {\n",
       "  background-color: #fa9656;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row8_col5 {\n",
       "  background-color: #96d268;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col0 {\n",
       "  background-color: #e6f59d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col1, #T_071e3_row11_col4 {\n",
       "  background-color: #fece7c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col2 {\n",
       "  background-color: #c3e67d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col3, #T_071e3_row19_col3 {\n",
       "  background-color: #e5f49b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col4 {\n",
       "  background-color: #fee28f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row9_col5 {\n",
       "  background-color: #e9f6a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row10_col2, #T_071e3_row17_col4 {\n",
       "  background-color: #15904c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row10_col3, #T_071e3_row12_col2 {\n",
       "  background-color: #fffcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row10_col4 {\n",
       "  background-color: #e54e35;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row10_col5 {\n",
       "  background-color: #6ec064;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row11_col1, #T_071e3_row12_col5 {\n",
       "  background-color: #fed07e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row11_col2 {\n",
       "  background-color: #9bd469;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row11_col3 {\n",
       "  background-color: #dff293;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row11_col5, #T_071e3_row21_col1 {\n",
       "  background-color: #d5ed88;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row12_col0 {\n",
       "  background-color: #66bd63;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row12_col3 {\n",
       "  background-color: #f4fab0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row12_col4 {\n",
       "  background-color: #d3ec87;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row13_col0 {\n",
       "  background-color: #f57547;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row13_col1, #T_071e3_row17_col1, #T_071e3_row19_col1 {\n",
       "  background-color: #fdbb6c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row13_col3 {\n",
       "  background-color: #ddf191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row13_col4, #T_071e3_row22_col5 {\n",
       "  background-color: #87cb67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row13_col5 {\n",
       "  background-color: #f67f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row14_col5 {\n",
       "  background-color: #fff6b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row15_col1 {\n",
       "  background-color: #fba35c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row15_col3 {\n",
       "  background-color: #c5e67e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row16_col0 {\n",
       "  background-color: #7fc866;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row16_col1 {\n",
       "  background-color: #fdb96a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row16_col2 {\n",
       "  background-color: #45ad5b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row16_col4, #T_071e3_row22_col4, #T_071e3_row23_col2 {\n",
       "  background-color: #f7844e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row16_col5 {\n",
       "  background-color: #89cc67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row17_col0 {\n",
       "  background-color: #e44c34;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row17_col2 {\n",
       "  background-color: #c82227;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row17_col5 {\n",
       "  background-color: #c21c27;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row18_col0 {\n",
       "  background-color: #db382b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row18_col1 {\n",
       "  background-color: #fca85e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row18_col5 {\n",
       "  background-color: #fff2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row19_col2, #T_071e3_row21_col5 {\n",
       "  background-color: #fee695;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row19_col4 {\n",
       "  background-color: #ecf7a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row19_col5 {\n",
       "  background-color: #fee08b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row20_col0 {\n",
       "  background-color: #128a49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row20_col1 {\n",
       "  background-color: #feea9b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row20_col3 {\n",
       "  background-color: #fff5ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row20_col4, #T_071e3_row21_col3 {\n",
       "  background-color: #fede89;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row21_col0 {\n",
       "  background-color: #8ccd67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row21_col2 {\n",
       "  background-color: #a0d669;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row21_col4 {\n",
       "  background-color: #e0f295;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row22_col0 {\n",
       "  background-color: #f36b42;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row22_col1 {\n",
       "  background-color: #fed27f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row22_col2 {\n",
       "  background-color: #249d53;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row23_col0, #T_071e3_row23_col3 {\n",
       "  background-color: #d7ee8a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row23_col4 {\n",
       "  background-color: #8ecf67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row23_col5 {\n",
       "  background-color: #f7814c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row24_col0 {\n",
       "  background-color: #0b7d42;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row24_col1 {\n",
       "  background-color: #fa9857;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row24_col2 {\n",
       "  background-color: #04703b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_071e3_row24_col3 {\n",
       "  background-color: #bbe278;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_071e3_row24_col5 {\n",
       "  background-color: #0e8245;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_071e3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_071e3_level0_col0\" class=\"col_heading level0 col0\" >const</th>\n",
       "      <th id=\"T_071e3_level0_col1\" class=\"col_heading level0 col1\" >MTUM</th>\n",
       "      <th id=\"T_071e3_level0_col2\" class=\"col_heading level0 col2\" >QUAL</th>\n",
       "      <th id=\"T_071e3_level0_col3\" class=\"col_heading level0 col3\" >SIZE</th>\n",
       "      <th id=\"T_071e3_level0_col4\" class=\"col_heading level0 col4\" >USMV</th>\n",
       "      <th id=\"T_071e3_level0_col5\" class=\"col_heading level0 col5\" >VLUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_071e3_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "      <td id=\"T_071e3_row0_col1\" class=\"data row0 col1\" >-0.6174</td>\n",
       "      <td id=\"T_071e3_row0_col2\" class=\"data row0 col2\" >0.3847</td>\n",
       "      <td id=\"T_071e3_row0_col3\" class=\"data row0 col3\" >1.0857</td>\n",
       "      <td id=\"T_071e3_row0_col4\" class=\"data row0 col4\" >-1.1434</td>\n",
       "      <td id=\"T_071e3_row0_col5\" class=\"data row0 col5\" >1.4537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_071e3_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "      <td id=\"T_071e3_row1_col1\" class=\"data row1 col1\" >0.2236</td>\n",
       "      <td id=\"T_071e3_row1_col2\" class=\"data row1 col2\" >0.3103</td>\n",
       "      <td id=\"T_071e3_row1_col3\" class=\"data row1 col3\" >0.2055</td>\n",
       "      <td id=\"T_071e3_row1_col4\" class=\"data row1 col4\" >-0.0523</td>\n",
       "      <td id=\"T_071e3_row1_col5\" class=\"data row1 col5\" >0.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_071e3_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "      <td id=\"T_071e3_row2_col1\" class=\"data row2 col1\" >0.3086</td>\n",
       "      <td id=\"T_071e3_row2_col2\" class=\"data row2 col2\" >0.1875</td>\n",
       "      <td id=\"T_071e3_row2_col3\" class=\"data row2 col3\" >0.0764</td>\n",
       "      <td id=\"T_071e3_row2_col4\" class=\"data row2 col4\" >0.5222</td>\n",
       "      <td id=\"T_071e3_row2_col5\" class=\"data row2 col5\" >-0.0462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_071e3_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "      <td id=\"T_071e3_row3_col1\" class=\"data row3 col1\" >0.0716</td>\n",
       "      <td id=\"T_071e3_row3_col2\" class=\"data row3 col2\" >0.1550</td>\n",
       "      <td id=\"T_071e3_row3_col3\" class=\"data row3 col3\" >0.2640</td>\n",
       "      <td id=\"T_071e3_row3_col4\" class=\"data row3 col4\" >0.3029</td>\n",
       "      <td id=\"T_071e3_row3_col5\" class=\"data row3 col5\" >0.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_071e3_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "      <td id=\"T_071e3_row4_col1\" class=\"data row4 col1\" >0.0825</td>\n",
       "      <td id=\"T_071e3_row4_col2\" class=\"data row4 col2\" >0.1750</td>\n",
       "      <td id=\"T_071e3_row4_col3\" class=\"data row4 col3\" >0.2755</td>\n",
       "      <td id=\"T_071e3_row4_col4\" class=\"data row4 col4\" >0.2802</td>\n",
       "      <td id=\"T_071e3_row4_col5\" class=\"data row4 col5\" >0.1424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_071e3_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "      <td id=\"T_071e3_row5_col1\" class=\"data row5 col1\" >-0.2225</td>\n",
       "      <td id=\"T_071e3_row5_col2\" class=\"data row5 col2\" >-0.0563</td>\n",
       "      <td id=\"T_071e3_row5_col3\" class=\"data row5 col3\" >0.5690</td>\n",
       "      <td id=\"T_071e3_row5_col4\" class=\"data row5 col4\" >1.1192</td>\n",
       "      <td id=\"T_071e3_row5_col5\" class=\"data row5 col5\" >-0.4869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_071e3_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "      <td id=\"T_071e3_row6_col1\" class=\"data row6 col1\" >-0.2158</td>\n",
       "      <td id=\"T_071e3_row6_col2\" class=\"data row6 col2\" >-0.0890</td>\n",
       "      <td id=\"T_071e3_row6_col3\" class=\"data row6 col3\" >0.5280</td>\n",
       "      <td id=\"T_071e3_row6_col4\" class=\"data row6 col4\" >1.1880</td>\n",
       "      <td id=\"T_071e3_row6_col5\" class=\"data row6 col5\" >-0.5770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_071e3_row7_col0\" class=\"data row7 col0\" >0.0005</td>\n",
       "      <td id=\"T_071e3_row7_col1\" class=\"data row7 col1\" >0.0756</td>\n",
       "      <td id=\"T_071e3_row7_col2\" class=\"data row7 col2\" >0.3323</td>\n",
       "      <td id=\"T_071e3_row7_col3\" class=\"data row7 col3\" >0.3341</td>\n",
       "      <td id=\"T_071e3_row7_col4\" class=\"data row7 col4\" >-0.3697</td>\n",
       "      <td id=\"T_071e3_row7_col5\" class=\"data row7 col5\" >0.7040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_071e3_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "      <td id=\"T_071e3_row8_col1\" class=\"data row8 col1\" >0.1799</td>\n",
       "      <td id=\"T_071e3_row8_col2\" class=\"data row8 col2\" >0.3985</td>\n",
       "      <td id=\"T_071e3_row8_col3\" class=\"data row8 col3\" >0.2753</td>\n",
       "      <td id=\"T_071e3_row8_col4\" class=\"data row8 col4\" >-0.4646</td>\n",
       "      <td id=\"T_071e3_row8_col5\" class=\"data row8 col5\" >0.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_071e3_row9_col0\" class=\"data row9 col0\" >0.0000</td>\n",
       "      <td id=\"T_071e3_row9_col1\" class=\"data row9 col1\" >-0.0535</td>\n",
       "      <td id=\"T_071e3_row9_col2\" class=\"data row9 col2\" >0.2147</td>\n",
       "      <td id=\"T_071e3_row9_col3\" class=\"data row9 col3\" >0.4089</td>\n",
       "      <td id=\"T_071e3_row9_col4\" class=\"data row9 col4\" >-0.0640</td>\n",
       "      <td id=\"T_071e3_row9_col5\" class=\"data row9 col5\" >0.4251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_071e3_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "      <td id=\"T_071e3_row10_col1\" class=\"data row10 col1\" >0.0537</td>\n",
       "      <td id=\"T_071e3_row10_col2\" class=\"data row10 col2\" >0.3676</td>\n",
       "      <td id=\"T_071e3_row10_col3\" class=\"data row10 col3\" >0.2851</td>\n",
       "      <td id=\"T_071e3_row10_col4\" class=\"data row10 col4\" >-0.7861</td>\n",
       "      <td id=\"T_071e3_row10_col5\" class=\"data row10 col5\" >0.9582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_071e3_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "      <td id=\"T_071e3_row11_col1\" class=\"data row11 col1\" >-0.0477</td>\n",
       "      <td id=\"T_071e3_row11_col2\" class=\"data row11 col2\" >0.2557</td>\n",
       "      <td id=\"T_071e3_row11_col3\" class=\"data row11 col3\" >0.4299</td>\n",
       "      <td id=\"T_071e3_row11_col4\" class=\"data row11 col4\" >-0.1855</td>\n",
       "      <td id=\"T_071e3_row11_col5\" class=\"data row11 col5\" >0.5469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_071e3_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "      <td id=\"T_071e3_row12_col1\" class=\"data row12 col1\" >0.0234</td>\n",
       "      <td id=\"T_071e3_row12_col2\" class=\"data row12 col2\" >0.1293</td>\n",
       "      <td id=\"T_071e3_row12_col3\" class=\"data row12 col3\" >0.3482</td>\n",
       "      <td id=\"T_071e3_row12_col4\" class=\"data row12 col4\" >0.4953</td>\n",
       "      <td id=\"T_071e3_row12_col5\" class=\"data row12 col5\" >-0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_071e3_row13_col0\" class=\"data row13 col0\" >-0.0003</td>\n",
       "      <td id=\"T_071e3_row13_col1\" class=\"data row13 col1\" >-0.1141</td>\n",
       "      <td id=\"T_071e3_row13_col2\" class=\"data row13 col2\" >-0.0021</td>\n",
       "      <td id=\"T_071e3_row13_col3\" class=\"data row13 col3\" >0.4376</td>\n",
       "      <td id=\"T_071e3_row13_col4\" class=\"data row13 col4\" >0.8810</td>\n",
       "      <td id=\"T_071e3_row13_col5\" class=\"data row13 col5\" >-0.3382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_071e3_row14_col0\" class=\"data row14 col0\" >0.0005</td>\n",
       "      <td id=\"T_071e3_row14_col1\" class=\"data row14 col1\" >0.9280</td>\n",
       "      <td id=\"T_071e3_row14_col2\" class=\"data row14 col2\" >0.4153</td>\n",
       "      <td id=\"T_071e3_row14_col3\" class=\"data row14 col3\" >-0.4857</td>\n",
       "      <td id=\"T_071e3_row14_col4\" class=\"data row14 col4\" >0.1509</td>\n",
       "      <td id=\"T_071e3_row14_col5\" class=\"data row14 col5\" >0.2270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_071e3_row15_col0\" class=\"data row15 col0\" >0.0000</td>\n",
       "      <td id=\"T_071e3_row15_col1\" class=\"data row15 col1\" >-0.1805</td>\n",
       "      <td id=\"T_071e3_row15_col2\" class=\"data row15 col2\" >-0.1459</td>\n",
       "      <td id=\"T_071e3_row15_col3\" class=\"data row15 col3\" >0.5152</td>\n",
       "      <td id=\"T_071e3_row15_col4\" class=\"data row15 col4\" >1.5802</td>\n",
       "      <td id=\"T_071e3_row15_col5\" class=\"data row15 col5\" >-0.8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_071e3_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "      <td id=\"T_071e3_row16_col1\" class=\"data row16 col1\" >-0.1179</td>\n",
       "      <td id=\"T_071e3_row16_col2\" class=\"data row16 col2\" >0.3275</td>\n",
       "      <td id=\"T_071e3_row16_col3\" class=\"data row16 col3\" >0.5273</td>\n",
       "      <td id=\"T_071e3_row16_col4\" class=\"data row16 col4\" >-0.5373</td>\n",
       "      <td id=\"T_071e3_row16_col5\" class=\"data row16 col5\" >0.8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_071e3_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "      <td id=\"T_071e3_row17_col1\" class=\"data row17 col1\" >-0.1124</td>\n",
       "      <td id=\"T_071e3_row17_col2\" class=\"data row17 col2\" >-0.1054</td>\n",
       "      <td id=\"T_071e3_row17_col3\" class=\"data row17 col3\" >0.4160</td>\n",
       "      <td id=\"T_071e3_row17_col4\" class=\"data row17 col4\" >1.3473</td>\n",
       "      <td id=\"T_071e3_row17_col5\" class=\"data row17 col5\" >-0.7230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_071e3_row18_col0\" class=\"data row18 col0\" >-0.0004</td>\n",
       "      <td id=\"T_071e3_row18_col1\" class=\"data row18 col1\" >-0.1698</td>\n",
       "      <td id=\"T_071e3_row18_col2\" class=\"data row18 col2\" >0.1456</td>\n",
       "      <td id=\"T_071e3_row18_col3\" class=\"data row18 col3\" >0.5654</td>\n",
       "      <td id=\"T_071e3_row18_col4\" class=\"data row18 col4\" >0.2944</td>\n",
       "      <td id=\"T_071e3_row18_col5\" class=\"data row18 col5\" >0.2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_071e3_row19_col0\" class=\"data row19 col0\" >0.0001</td>\n",
       "      <td id=\"T_071e3_row19_col1\" class=\"data row19 col1\" >-0.1114</td>\n",
       "      <td id=\"T_071e3_row19_col2\" class=\"data row19 col2\" >0.0902</td>\n",
       "      <td id=\"T_071e3_row19_col3\" class=\"data row19 col3\" >0.4059</td>\n",
       "      <td id=\"T_071e3_row19_col4\" class=\"data row19 col4\" >0.3260</td>\n",
       "      <td id=\"T_071e3_row19_col5\" class=\"data row19 col5\" >0.0646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_071e3_row20_col0\" class=\"data row20 col0\" >0.0004</td>\n",
       "      <td id=\"T_071e3_row20_col1\" class=\"data row20 col1\" >0.0523</td>\n",
       "      <td id=\"T_071e3_row20_col2\" class=\"data row20 col2\" >0.2066</td>\n",
       "      <td id=\"T_071e3_row20_col3\" class=\"data row20 col3\" >0.2454</td>\n",
       "      <td id=\"T_071e3_row20_col4\" class=\"data row20 col4\" >-0.0941</td>\n",
       "      <td id=\"T_071e3_row20_col5\" class=\"data row20 col5\" >0.3757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_071e3_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "      <td id=\"T_071e3_row21_col1\" class=\"data row21 col1\" >0.3213</td>\n",
       "      <td id=\"T_071e3_row21_col2\" class=\"data row21 col2\" >0.2528</td>\n",
       "      <td id=\"T_071e3_row21_col3\" class=\"data row21 col3\" >0.1344</td>\n",
       "      <td id=\"T_071e3_row21_col4\" class=\"data row21 col4\" >0.4176</td>\n",
       "      <td id=\"T_071e3_row21_col5\" class=\"data row21 col5\" >0.1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_071e3_row22_col0\" class=\"data row22 col0\" >-0.0004</td>\n",
       "      <td id=\"T_071e3_row22_col1\" class=\"data row22 col1\" >-0.0401</td>\n",
       "      <td id=\"T_071e3_row22_col2\" class=\"data row22 col2\" >0.3498</td>\n",
       "      <td id=\"T_071e3_row22_col3\" class=\"data row22 col3\" >0.4581</td>\n",
       "      <td id=\"T_071e3_row22_col4\" class=\"data row22 col4\" >-0.5477</td>\n",
       "      <td id=\"T_071e3_row22_col5\" class=\"data row22 col5\" >0.8658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_071e3_row23_col0\" class=\"data row23 col0\" >0.0001</td>\n",
       "      <td id=\"T_071e3_row23_col1\" class=\"data row23 col1\" >-0.1570</td>\n",
       "      <td id=\"T_071e3_row23_col2\" class=\"data row23 col2\" >-0.0138</td>\n",
       "      <td id=\"T_071e3_row23_col3\" class=\"data row23 col3\" >0.4624</td>\n",
       "      <td id=\"T_071e3_row23_col4\" class=\"data row23 col4\" >0.8432</td>\n",
       "      <td id=\"T_071e3_row23_col5\" class=\"data row23 col5\" >-0.3221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_071e3_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_071e3_row24_col0\" class=\"data row24 col0\" >0.0005</td>\n",
       "      <td id=\"T_071e3_row24_col1\" class=\"data row24 col1\" >-0.2022</td>\n",
       "      <td id=\"T_071e3_row24_col2\" class=\"data row24 col2\" >0.4050</td>\n",
       "      <td id=\"T_071e3_row24_col3\" class=\"data row24 col3\" >0.5509</td>\n",
       "      <td id=\"T_071e3_row24_col4\" class=\"data row24 col4\" >-1.1994</td>\n",
       "      <td id=\"T_071e3_row24_col5\" class=\"data row24 col5\" >1.3225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x31371ccd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "loadings = rp.loadings_matrix(X=X, Y=Y, feature_selection=feature_selection,\n",
    "                              n_components=n_components)\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2498%</td>\n",
       "      <td>11.0551%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.4527%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5016%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9345%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.0770%</td>\n",
       "      <td>0.8006%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.7562%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA      CNP     CPB      DE  \\\n",
       "weights 0.0000% 5.2498% 11.0551% 0.0000% 0.0000% 10.4527% 0.0000% 4.5016%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.9345% 0.0000% 0.0000% 0.0000% 0.0000% 5.0770%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.8006% 0.0000% 5.7562% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building the portfolio object\n",
    "port = rp.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimal portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu,\n",
    "                  method_cov=method_cov)\n",
    "\n",
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu,\n",
    "                   method_cov=method_cov,\n",
    "                   dict_load=dict(feature_selection=feature_selection,\n",
    "                                  n_components=n_components)\n",
    "                  )\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='FM' # Factor Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = rp.plot_pie(w=w, title='Sharpe FM Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                 height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7625%</td>\n",
       "      <td>2.8313%</td>\n",
       "      <td>3.0976%</td>\n",
       "      <td>9.6894%</td>\n",
       "      <td>5.7296%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.9362%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.5892%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.4000%</td>\n",
       "      <td>1.1688%</td>\n",
       "      <td>10.6155%</td>\n",
       "      <td>3.1996%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.5815%</td>\n",
       "      <td>2.2333%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4136%</td>\n",
       "      <td>5.2908%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.9053%</td>\n",
       "      <td>11.5654%</td>\n",
       "      <td>3.7706%</td>\n",
       "      <td>0.9451%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.1462%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.1671%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.8156%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.9541%</td>\n",
       "      <td>4.1886%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.6605%</td>\n",
       "      <td>2.1756%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.3810%</td>\n",
       "      <td>6.8442%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.5560%</td>\n",
       "      <td>12.2104%</td>\n",
       "      <td>2.8637%</td>\n",
       "      <td>1.6897%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.1264%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.7358%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2744%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.0140%</td>\n",
       "      <td>4.5027%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.5149%</td>\n",
       "      <td>1.8258%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9101%</td>\n",
       "      <td>7.6412%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.0734%</td>\n",
       "      <td>12.5671%</td>\n",
       "      <td>2.1089%</td>\n",
       "      <td>2.0795%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0144%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.1619%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1452%</td>\n",
       "      <td>4.6662%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.2582%</td>\n",
       "      <td>1.4426%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.3872%</td>\n",
       "      <td>8.3345%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.5725%</td>\n",
       "      <td>12.8057%</td>\n",
       "      <td>1.3521%</td>\n",
       "      <td>2.4264%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.1586%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0716%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.2560%</td>\n",
       "      <td>4.7999%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>11.9231%</td>\n",
       "      <td>1.0605%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 0.7625% 2.8313% 3.0976%  9.6894% 5.7296% 0.0000% 0.0000%   \n",
       "1 0.0000% 0.4136% 5.2908% 0.0000% 2.9053% 11.5654% 3.7706% 0.9451% 0.0000%   \n",
       "2 0.0000% 1.3810% 6.8442% 0.0000% 2.5560% 12.2104% 2.8637% 1.6897% 0.0000%   \n",
       "3 0.0000% 1.9101% 7.6412% 0.0000% 2.0734% 12.5671% 2.1089% 2.0795% 0.0000%   \n",
       "4 0.0000% 2.3872% 8.3345% 0.0000% 1.5725% 12.8057% 1.3521% 2.4264% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE        T     TGT     TMO  \\\n",
       "0 2.9362%  ... 12.5892% 0.0000% 12.4000% 1.1688% 10.6155% 3.1996% 0.0000%   \n",
       "1 1.1462%  ... 14.1671% 0.0000%  6.8156% 0.0000%  8.9541% 4.1886% 0.0000%   \n",
       "2 0.1264%  ... 14.7358% 0.0000%  4.2744% 0.0000%  8.0140% 4.5027% 0.0000%   \n",
       "3 0.0000%  ... 15.0144% 0.0000%  2.1619% 0.0000%  7.1452% 4.6662% 0.0000%   \n",
       "4 0.0000%  ... 15.1586% 0.0000%  0.0716% 0.0000%  6.2560% 4.7999% 0.0000%   \n",
       "\n",
       "      TXT       VZ    ZION  \n",
       "0 0.0000% 12.5815% 2.2333%  \n",
       "1 0.0000% 12.6605% 2.1756%  \n",
       "2 0.0000% 12.5149% 1.8258%  \n",
       "3 0.0000% 12.2582% 1.4426%  \n",
       "4 0.0000% 11.9231% 1.0605%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu_fm # Expected returns\n",
    "cov = port.cov_fm # Covariance matrix\n",
    "returns = port.returns_fm # Returns of the assets\n",
    "\n",
    "ax = rp.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                      rf=rf, alpha=0.01, cmap='viridis', w=w, label=label,\n",
    "                      marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = rp.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Portfolios Using Risk Factors with Other Risk Measures and PCR\n",
    "\n",
    "In this part I will calculate optimal portfolios for several risk measures using a __mean estimate based on PCR__. I will find the portfolios that maximize the risk adjusted return for all available risk measures.\n",
    "\n",
    "### 3.1 Calculate Optimal Portfolios for Several Risk Measures.\n",
    "\n",
    "I will mantain the constraints on risk factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = False\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_86ab0_row0_col0, #T_86ab0_row0_col1, #T_86ab0_row0_col2, #T_86ab0_row0_col3, #T_86ab0_row0_col4, #T_86ab0_row0_col5, #T_86ab0_row0_col6, #T_86ab0_row0_col7, #T_86ab0_row0_col8, #T_86ab0_row0_col9, #T_86ab0_row0_col10, #T_86ab0_row0_col11, #T_86ab0_row0_col12, #T_86ab0_row1_col1, #T_86ab0_row1_col2, #T_86ab0_row1_col3, #T_86ab0_row1_col4, #T_86ab0_row1_col5, #T_86ab0_row1_col6, #T_86ab0_row1_col7, #T_86ab0_row1_col8, #T_86ab0_row1_col9, #T_86ab0_row1_col10, #T_86ab0_row1_col11, #T_86ab0_row1_col12, #T_86ab0_row2_col1, #T_86ab0_row2_col2, #T_86ab0_row2_col3, #T_86ab0_row2_col4, #T_86ab0_row2_col5, #T_86ab0_row2_col6, #T_86ab0_row2_col7, #T_86ab0_row2_col8, #T_86ab0_row2_col9, #T_86ab0_row2_col10, #T_86ab0_row2_col11, #T_86ab0_row2_col12, #T_86ab0_row3_col0, #T_86ab0_row3_col1, #T_86ab0_row3_col2, #T_86ab0_row3_col3, #T_86ab0_row3_col4, #T_86ab0_row3_col5, #T_86ab0_row3_col6, #T_86ab0_row3_col7, #T_86ab0_row3_col8, #T_86ab0_row3_col9, #T_86ab0_row3_col10, #T_86ab0_row3_col11, #T_86ab0_row3_col12, #T_86ab0_row4_col0, #T_86ab0_row4_col1, #T_86ab0_row4_col2, #T_86ab0_row4_col3, #T_86ab0_row4_col4, #T_86ab0_row4_col5, #T_86ab0_row4_col6, #T_86ab0_row4_col7, #T_86ab0_row4_col8, #T_86ab0_row4_col9, #T_86ab0_row4_col10, #T_86ab0_row4_col11, #T_86ab0_row4_col12, #T_86ab0_row5_col1, #T_86ab0_row5_col2, #T_86ab0_row5_col3, #T_86ab0_row5_col4, #T_86ab0_row5_col5, #T_86ab0_row5_col6, #T_86ab0_row5_col7, #T_86ab0_row5_col8, #T_86ab0_row5_col9, #T_86ab0_row5_col10, #T_86ab0_row5_col11, #T_86ab0_row5_col12, #T_86ab0_row6_col0, #T_86ab0_row6_col1, #T_86ab0_row6_col2, #T_86ab0_row6_col3, #T_86ab0_row6_col4, #T_86ab0_row6_col5, #T_86ab0_row6_col6, #T_86ab0_row6_col7, #T_86ab0_row6_col8, #T_86ab0_row6_col9, #T_86ab0_row6_col10, #T_86ab0_row6_col11, #T_86ab0_row6_col12, #T_86ab0_row7_col1, #T_86ab0_row7_col2, #T_86ab0_row7_col3, #T_86ab0_row7_col4, #T_86ab0_row7_col5, #T_86ab0_row7_col6, #T_86ab0_row7_col7, #T_86ab0_row7_col8, #T_86ab0_row7_col9, #T_86ab0_row7_col10, #T_86ab0_row7_col11, #T_86ab0_row7_col12, #T_86ab0_row8_col0, #T_86ab0_row8_col1, #T_86ab0_row8_col2, #T_86ab0_row8_col3, #T_86ab0_row8_col4, #T_86ab0_row8_col5, #T_86ab0_row8_col6, #T_86ab0_row8_col7, #T_86ab0_row8_col8, #T_86ab0_row8_col9, #T_86ab0_row8_col10, #T_86ab0_row8_col11, #T_86ab0_row8_col12, #T_86ab0_row9_col0, #T_86ab0_row9_col1, #T_86ab0_row9_col2, #T_86ab0_row9_col3, #T_86ab0_row9_col4, #T_86ab0_row9_col5, #T_86ab0_row9_col6, #T_86ab0_row9_col7, #T_86ab0_row9_col8, #T_86ab0_row9_col9, #T_86ab0_row9_col10, #T_86ab0_row9_col11, #T_86ab0_row9_col12, #T_86ab0_row10_col1, #T_86ab0_row10_col2, #T_86ab0_row10_col3, #T_86ab0_row10_col4, #T_86ab0_row10_col5, #T_86ab0_row10_col6, #T_86ab0_row10_col7, #T_86ab0_row10_col8, #T_86ab0_row10_col9, #T_86ab0_row10_col10, #T_86ab0_row10_col11, #T_86ab0_row10_col12, #T_86ab0_row11_col0, #T_86ab0_row11_col1, #T_86ab0_row11_col2, #T_86ab0_row11_col3, #T_86ab0_row11_col4, #T_86ab0_row11_col5, #T_86ab0_row11_col6, #T_86ab0_row11_col7, #T_86ab0_row11_col8, #T_86ab0_row11_col9, #T_86ab0_row11_col10, #T_86ab0_row11_col11, #T_86ab0_row11_col12, #T_86ab0_row12_col1, #T_86ab0_row12_col2, #T_86ab0_row12_col3, #T_86ab0_row12_col4, #T_86ab0_row12_col5, #T_86ab0_row12_col6, #T_86ab0_row12_col7, #T_86ab0_row12_col8, #T_86ab0_row12_col9, #T_86ab0_row12_col10, #T_86ab0_row12_col11, #T_86ab0_row12_col12, #T_86ab0_row13_col0, #T_86ab0_row13_col1, #T_86ab0_row13_col2, #T_86ab0_row13_col3, #T_86ab0_row13_col4, #T_86ab0_row13_col5, #T_86ab0_row13_col6, #T_86ab0_row13_col7, #T_86ab0_row13_col8, #T_86ab0_row13_col9, #T_86ab0_row13_col10, #T_86ab0_row13_col11, #T_86ab0_row13_col12, #T_86ab0_row14_col1, #T_86ab0_row14_col2, #T_86ab0_row14_col3, #T_86ab0_row14_col4, #T_86ab0_row14_col5, #T_86ab0_row14_col6, #T_86ab0_row14_col7, #T_86ab0_row14_col9, #T_86ab0_row14_col10, #T_86ab0_row14_col11, #T_86ab0_row15_col7, #T_86ab0_row16_col0, #T_86ab0_row16_col1, #T_86ab0_row16_col2, #T_86ab0_row16_col3, #T_86ab0_row16_col4, #T_86ab0_row16_col5, #T_86ab0_row16_col6, #T_86ab0_row16_col7, #T_86ab0_row16_col8, #T_86ab0_row16_col9, #T_86ab0_row16_col10, #T_86ab0_row16_col11, #T_86ab0_row16_col12, #T_86ab0_row17_col0, #T_86ab0_row17_col1, #T_86ab0_row17_col2, #T_86ab0_row17_col3, #T_86ab0_row17_col4, #T_86ab0_row17_col5, #T_86ab0_row17_col6, #T_86ab0_row17_col7, #T_86ab0_row17_col8, #T_86ab0_row17_col9, #T_86ab0_row17_col10, #T_86ab0_row17_col11, #T_86ab0_row17_col12, #T_86ab0_row18_col0, #T_86ab0_row18_col1, #T_86ab0_row18_col2, #T_86ab0_row18_col3, #T_86ab0_row18_col4, #T_86ab0_row18_col5, #T_86ab0_row18_col6, #T_86ab0_row18_col7, #T_86ab0_row18_col8, #T_86ab0_row18_col9, #T_86ab0_row18_col10, #T_86ab0_row18_col11, #T_86ab0_row18_col12, #T_86ab0_row19_col0, #T_86ab0_row19_col1, #T_86ab0_row19_col2, #T_86ab0_row19_col3, #T_86ab0_row19_col4, #T_86ab0_row19_col5, #T_86ab0_row19_col6, #T_86ab0_row19_col7, #T_86ab0_row19_col8, #T_86ab0_row19_col9, #T_86ab0_row19_col10, #T_86ab0_row19_col11, #T_86ab0_row19_col12, #T_86ab0_row20_col8, #T_86ab0_row21_col1, #T_86ab0_row21_col2, #T_86ab0_row21_col3, #T_86ab0_row21_col4, #T_86ab0_row21_col5, #T_86ab0_row21_col6, #T_86ab0_row21_col7, #T_86ab0_row21_col8, #T_86ab0_row21_col9, #T_86ab0_row21_col10, #T_86ab0_row21_col11, #T_86ab0_row21_col12, #T_86ab0_row22_col0, #T_86ab0_row22_col1, #T_86ab0_row22_col2, #T_86ab0_row22_col3, #T_86ab0_row22_col4, #T_86ab0_row22_col5, #T_86ab0_row22_col6, #T_86ab0_row22_col7, #T_86ab0_row22_col8, #T_86ab0_row22_col9, #T_86ab0_row22_col10, #T_86ab0_row22_col11, #T_86ab0_row22_col12, #T_86ab0_row23_col1, #T_86ab0_row23_col2, #T_86ab0_row23_col3, #T_86ab0_row23_col4, #T_86ab0_row23_col5, #T_86ab0_row23_col6, #T_86ab0_row23_col7, #T_86ab0_row23_col8, #T_86ab0_row23_col9, #T_86ab0_row23_col10, #T_86ab0_row23_col11, #T_86ab0_row23_col12, #T_86ab0_row24_col0, #T_86ab0_row24_col1, #T_86ab0_row24_col2, #T_86ab0_row24_col3, #T_86ab0_row24_col4, #T_86ab0_row24_col5, #T_86ab0_row24_col6, #T_86ab0_row24_col7, #T_86ab0_row24_col8, #T_86ab0_row24_col9, #T_86ab0_row24_col10, #T_86ab0_row24_col11, #T_86ab0_row24_col12 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row1_col0 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row2_col0 {\n",
       "  background-color: #64bc6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row5_col0 {\n",
       "  background-color: #72c376;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row7_col0 {\n",
       "  background-color: #e0f3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row10_col0 {\n",
       "  background-color: #70c275;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row12_col0, #T_86ab0_row15_col8, #T_86ab0_row20_col1, #T_86ab0_row20_col2, #T_86ab0_row20_col3, #T_86ab0_row20_col4, #T_86ab0_row20_col5, #T_86ab0_row20_col6, #T_86ab0_row20_col7, #T_86ab0_row20_col9, #T_86ab0_row20_col10, #T_86ab0_row20_col11, #T_86ab0_row20_col12 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row14_col0 {\n",
       "  background-color: #228343;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row14_col8 {\n",
       "  background-color: #14783e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row14_col12 {\n",
       "  background-color: #e8f6ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col0 {\n",
       "  background-color: #68be71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col1 {\n",
       "  background-color: #b6e192;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col2 {\n",
       "  background-color: #9cd687;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col3 {\n",
       "  background-color: #c0e597;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col4 {\n",
       "  background-color: #97d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col5 {\n",
       "  background-color: #39a056;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row15_col6 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col9, #T_86ab0_row15_col10 {\n",
       "  background-color: #8dcf81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col11 {\n",
       "  background-color: #84cb7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row15_col12 {\n",
       "  background-color: #369d54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_86ab0_row20_col0 {\n",
       "  background-color: #daf0a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row21_col0 {\n",
       "  background-color: #fcfed7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_86ab0_row23_col0 {\n",
       "  background-color: #ceeb9e;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_86ab0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_86ab0_level0_col0\" class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th id=\"T_86ab0_level0_col1\" class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th id=\"T_86ab0_level0_col2\" class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th id=\"T_86ab0_level0_col3\" class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th id=\"T_86ab0_level0_col4\" class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th id=\"T_86ab0_level0_col5\" class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th id=\"T_86ab0_level0_col6\" class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th id=\"T_86ab0_level0_col7\" class=\"col_heading level0 col7\" >WR</th>\n",
       "      <th id=\"T_86ab0_level0_col8\" class=\"col_heading level0 col8\" >MDD</th>\n",
       "      <th id=\"T_86ab0_level0_col9\" class=\"col_heading level0 col9\" >ADD</th>\n",
       "      <th id=\"T_86ab0_level0_col10\" class=\"col_heading level0 col10\" >CDaR</th>\n",
       "      <th id=\"T_86ab0_level0_col11\" class=\"col_heading level0 col11\" >UCI</th>\n",
       "      <th id=\"T_86ab0_level0_col12\" class=\"col_heading level0 col12\" >EDaR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_86ab0_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_86ab0_row1_col0\" class=\"data row1 col0\" >5.25%</td>\n",
       "      <td id=\"T_86ab0_row1_col1\" class=\"data row1 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col2\" class=\"data row1 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col3\" class=\"data row1 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col9\" class=\"data row1 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col10\" class=\"data row1 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col11\" class=\"data row1 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_86ab0_row2_col0\" class=\"data row2 col0\" >11.06%</td>\n",
       "      <td id=\"T_86ab0_row2_col1\" class=\"data row2 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col2\" class=\"data row2 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col4\" class=\"data row2 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col5\" class=\"data row2 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col6\" class=\"data row2 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col9\" class=\"data row2 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col11\" class=\"data row2 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_86ab0_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_86ab0_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_86ab0_row5_col0\" class=\"data row5 col0\" >10.45%</td>\n",
       "      <td id=\"T_86ab0_row5_col1\" class=\"data row5 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col2\" class=\"data row5 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col3\" class=\"data row5 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col4\" class=\"data row5 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col5\" class=\"data row5 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col6\" class=\"data row5 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col8\" class=\"data row5 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col9\" class=\"data row5 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col10\" class=\"data row5 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col11\" class=\"data row5 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row5_col12\" class=\"data row5 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_86ab0_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col7\" class=\"data row6 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_86ab0_row7_col0\" class=\"data row7 col0\" >4.50%</td>\n",
       "      <td id=\"T_86ab0_row7_col1\" class=\"data row7 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col2\" class=\"data row7 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col3\" class=\"data row7 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col4\" class=\"data row7 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col5\" class=\"data row7 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col8\" class=\"data row7 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col9\" class=\"data row7 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col10\" class=\"data row7 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_86ab0_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_86ab0_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_86ab0_row10_col0\" class=\"data row10 col0\" >10.52%</td>\n",
       "      <td id=\"T_86ab0_row10_col1\" class=\"data row10 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col2\" class=\"data row10 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col3\" class=\"data row10 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col4\" class=\"data row10 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_86ab0_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_86ab0_row12_col0\" class=\"data row12 col0\" >20.35%</td>\n",
       "      <td id=\"T_86ab0_row12_col1\" class=\"data row12 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col2\" class=\"data row12 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col3\" class=\"data row12 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col4\" class=\"data row12 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col5\" class=\"data row12 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col6\" class=\"data row12 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col7\" class=\"data row12 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col9\" class=\"data row12 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col10\" class=\"data row12 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col11\" class=\"data row12 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_86ab0_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_86ab0_row14_col0\" class=\"data row14 col0\" >15.30%</td>\n",
       "      <td id=\"T_86ab0_row14_col1\" class=\"data row14 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col2\" class=\"data row14 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col3\" class=\"data row14 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col4\" class=\"data row14 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col5\" class=\"data row14 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col6\" class=\"data row14 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col7\" class=\"data row14 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col8\" class=\"data row14 col8\" >44.55%</td>\n",
       "      <td id=\"T_86ab0_row14_col9\" class=\"data row14 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col10\" class=\"data row14 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col11\" class=\"data row14 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row14_col12\" class=\"data row14 col12\" >10.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_86ab0_row15_col0\" class=\"data row15 col0\" >10.93%</td>\n",
       "      <td id=\"T_86ab0_row15_col1\" class=\"data row15 col1\" >25.97%</td>\n",
       "      <td id=\"T_86ab0_row15_col2\" class=\"data row15 col2\" >29.45%</td>\n",
       "      <td id=\"T_86ab0_row15_col3\" class=\"data row15 col3\" >24.34%</td>\n",
       "      <td id=\"T_86ab0_row15_col4\" class=\"data row15 col4\" >29.91%</td>\n",
       "      <td id=\"T_86ab0_row15_col5\" class=\"data row15 col5\" >39.64%</td>\n",
       "      <td id=\"T_86ab0_row15_col6\" class=\"data row15 col6\" >16.43%</td>\n",
       "      <td id=\"T_86ab0_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row15_col8\" class=\"data row15 col8\" >55.45%</td>\n",
       "      <td id=\"T_86ab0_row15_col9\" class=\"data row15 col9\" >31.05%</td>\n",
       "      <td id=\"T_86ab0_row15_col10\" class=\"data row15 col10\" >31.05%</td>\n",
       "      <td id=\"T_86ab0_row15_col11\" class=\"data row15 col11\" >32.09%</td>\n",
       "      <td id=\"T_86ab0_row15_col12\" class=\"data row15 col12\" >36.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_86ab0_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_86ab0_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_86ab0_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_86ab0_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col1\" class=\"data row19 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col3\" class=\"data row19 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_86ab0_row20_col0\" class=\"data row20 col0\" >5.08%</td>\n",
       "      <td id=\"T_86ab0_row20_col1\" class=\"data row20 col1\" >74.03%</td>\n",
       "      <td id=\"T_86ab0_row20_col2\" class=\"data row20 col2\" >70.55%</td>\n",
       "      <td id=\"T_86ab0_row20_col3\" class=\"data row20 col3\" >75.66%</td>\n",
       "      <td id=\"T_86ab0_row20_col4\" class=\"data row20 col4\" >70.09%</td>\n",
       "      <td id=\"T_86ab0_row20_col5\" class=\"data row20 col5\" >60.36%</td>\n",
       "      <td id=\"T_86ab0_row20_col6\" class=\"data row20 col6\" >83.57%</td>\n",
       "      <td id=\"T_86ab0_row20_col7\" class=\"data row20 col7\" >100.00%</td>\n",
       "      <td id=\"T_86ab0_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row20_col9\" class=\"data row20 col9\" >68.95%</td>\n",
       "      <td id=\"T_86ab0_row20_col10\" class=\"data row20 col10\" >68.95%</td>\n",
       "      <td id=\"T_86ab0_row20_col11\" class=\"data row20 col11\" >67.91%</td>\n",
       "      <td id=\"T_86ab0_row20_col12\" class=\"data row20 col12\" >53.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_86ab0_row21_col0\" class=\"data row21 col0\" >0.80%</td>\n",
       "      <td id=\"T_86ab0_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col9\" class=\"data row21 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_86ab0_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_86ab0_row23_col0\" class=\"data row23 col0\" >5.76%</td>\n",
       "      <td id=\"T_86ab0_row23_col1\" class=\"data row23 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col2\" class=\"data row23 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col3\" class=\"data row23 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col4\" class=\"data row23 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col5\" class=\"data row23 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col9\" class=\"data row23 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col10\" class=\"data row23 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col11\" class=\"data row23 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row23_col12\" class=\"data row23 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_86ab0_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_86ab0_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "      <td id=\"T_86ab0_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x3171d8290>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "# based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = True\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c5c88_row0_col0, #T_c5c88_row0_col1, #T_c5c88_row0_col2, #T_c5c88_row0_col3, #T_c5c88_row0_col4, #T_c5c88_row0_col5, #T_c5c88_row0_col6, #T_c5c88_row0_col7, #T_c5c88_row0_col8, #T_c5c88_row0_col9, #T_c5c88_row0_col10, #T_c5c88_row0_col11, #T_c5c88_row0_col12, #T_c5c88_row1_col5, #T_c5c88_row1_col7, #T_c5c88_row1_col12, #T_c5c88_row2_col7, #T_c5c88_row2_col8, #T_c5c88_row2_col10, #T_c5c88_row2_col12, #T_c5c88_row3_col0, #T_c5c88_row3_col1, #T_c5c88_row3_col2, #T_c5c88_row3_col3, #T_c5c88_row3_col4, #T_c5c88_row3_col5, #T_c5c88_row3_col6, #T_c5c88_row3_col7, #T_c5c88_row3_col8, #T_c5c88_row3_col9, #T_c5c88_row3_col10, #T_c5c88_row3_col11, #T_c5c88_row3_col12, #T_c5c88_row4_col0, #T_c5c88_row4_col1, #T_c5c88_row4_col2, #T_c5c88_row4_col3, #T_c5c88_row4_col4, #T_c5c88_row4_col5, #T_c5c88_row4_col6, #T_c5c88_row4_col7, #T_c5c88_row4_col8, #T_c5c88_row4_col9, #T_c5c88_row4_col10, #T_c5c88_row4_col11, #T_c5c88_row4_col12, #T_c5c88_row6_col0, #T_c5c88_row6_col1, #T_c5c88_row6_col2, #T_c5c88_row6_col3, #T_c5c88_row6_col4, #T_c5c88_row6_col5, #T_c5c88_row6_col6, #T_c5c88_row6_col8, #T_c5c88_row6_col9, #T_c5c88_row6_col10, #T_c5c88_row6_col11, #T_c5c88_row6_col12, #T_c5c88_row7_col6, #T_c5c88_row7_col7, #T_c5c88_row8_col0, #T_c5c88_row8_col1, #T_c5c88_row8_col2, #T_c5c88_row8_col3, #T_c5c88_row8_col4, #T_c5c88_row8_col5, #T_c5c88_row8_col6, #T_c5c88_row8_col7, #T_c5c88_row8_col8, #T_c5c88_row8_col9, #T_c5c88_row8_col10, #T_c5c88_row8_col11, #T_c5c88_row8_col12, #T_c5c88_row9_col0, #T_c5c88_row9_col1, #T_c5c88_row9_col2, #T_c5c88_row9_col3, #T_c5c88_row9_col4, #T_c5c88_row9_col5, #T_c5c88_row9_col6, #T_c5c88_row9_col7, #T_c5c88_row9_col8, #T_c5c88_row9_col9, #T_c5c88_row9_col10, #T_c5c88_row9_col11, #T_c5c88_row9_col12, #T_c5c88_row10_col5, #T_c5c88_row10_col6, #T_c5c88_row10_col7, #T_c5c88_row10_col8, #T_c5c88_row10_col9, #T_c5c88_row10_col10, #T_c5c88_row10_col11, #T_c5c88_row10_col12, #T_c5c88_row11_col0, #T_c5c88_row11_col1, #T_c5c88_row11_col2, #T_c5c88_row11_col3, #T_c5c88_row11_col4, #T_c5c88_row11_col5, #T_c5c88_row11_col6, #T_c5c88_row11_col7, #T_c5c88_row11_col8, #T_c5c88_row11_col9, #T_c5c88_row11_col10, #T_c5c88_row11_col11, #T_c5c88_row11_col12, #T_c5c88_row12_col8, #T_c5c88_row12_col12, #T_c5c88_row13_col0, #T_c5c88_row13_col1, #T_c5c88_row13_col2, #T_c5c88_row13_col3, #T_c5c88_row13_col4, #T_c5c88_row13_col5, #T_c5c88_row13_col6, #T_c5c88_row13_col7, #T_c5c88_row13_col8, #T_c5c88_row13_col9, #T_c5c88_row13_col10, #T_c5c88_row13_col11, #T_c5c88_row13_col12, #T_c5c88_row15_col6, #T_c5c88_row15_col7, #T_c5c88_row15_col8, #T_c5c88_row16_col0, #T_c5c88_row16_col1, #T_c5c88_row16_col2, #T_c5c88_row16_col3, #T_c5c88_row16_col4, #T_c5c88_row16_col5, #T_c5c88_row16_col6, #T_c5c88_row16_col8, #T_c5c88_row16_col9, #T_c5c88_row16_col10, #T_c5c88_row16_col11, #T_c5c88_row16_col12, #T_c5c88_row17_col0, #T_c5c88_row17_col1, #T_c5c88_row17_col2, #T_c5c88_row17_col3, #T_c5c88_row17_col4, #T_c5c88_row17_col5, #T_c5c88_row17_col6, #T_c5c88_row17_col7, #T_c5c88_row17_col8, #T_c5c88_row17_col9, #T_c5c88_row17_col10, #T_c5c88_row17_col11, #T_c5c88_row17_col12, #T_c5c88_row18_col0, #T_c5c88_row18_col1, #T_c5c88_row18_col2, #T_c5c88_row18_col3, #T_c5c88_row18_col4, #T_c5c88_row18_col5, #T_c5c88_row18_col6, #T_c5c88_row18_col7, #T_c5c88_row18_col8, #T_c5c88_row18_col9, #T_c5c88_row18_col10, #T_c5c88_row18_col11, #T_c5c88_row18_col12, #T_c5c88_row19_col0, #T_c5c88_row19_col2, #T_c5c88_row19_col4, #T_c5c88_row19_col5, #T_c5c88_row19_col6, #T_c5c88_row19_col7, #T_c5c88_row19_col8, #T_c5c88_row19_col10, #T_c5c88_row19_col11, #T_c5c88_row19_col12, #T_c5c88_row20_col8, #T_c5c88_row20_col10, #T_c5c88_row21_col0, #T_c5c88_row21_col1, #T_c5c88_row21_col2, #T_c5c88_row21_col3, #T_c5c88_row21_col4, #T_c5c88_row21_col5, #T_c5c88_row21_col6, #T_c5c88_row21_col7, #T_c5c88_row21_col8, #T_c5c88_row21_col10, #T_c5c88_row21_col11, #T_c5c88_row21_col12, #T_c5c88_row22_col0, #T_c5c88_row22_col1, #T_c5c88_row22_col2, #T_c5c88_row22_col3, #T_c5c88_row22_col4, #T_c5c88_row22_col5, #T_c5c88_row22_col6, #T_c5c88_row22_col7, #T_c5c88_row22_col8, #T_c5c88_row22_col9, #T_c5c88_row22_col10, #T_c5c88_row22_col11, #T_c5c88_row22_col12, #T_c5c88_row23_col6, #T_c5c88_row23_col7, #T_c5c88_row23_col8, #T_c5c88_row24_col0, #T_c5c88_row24_col1, #T_c5c88_row24_col2, #T_c5c88_row24_col3, #T_c5c88_row24_col4, #T_c5c88_row24_col5, #T_c5c88_row24_col6, #T_c5c88_row24_col7, #T_c5c88_row24_col8, #T_c5c88_row24_col9, #T_c5c88_row24_col10, #T_c5c88_row24_col11, #T_c5c88_row24_col12 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col0 {\n",
       "  background-color: #ccea9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col1 {\n",
       "  background-color: #b5e092;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col2 {\n",
       "  background-color: #e7f6ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col3 {\n",
       "  background-color: #c8e99b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col4 {\n",
       "  background-color: #ecf7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col6 {\n",
       "  background-color: #fcfed6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col8 {\n",
       "  background-color: #feffde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col9, #T_c5c88_row6_col7, #T_c5c88_row23_col10 {\n",
       "  background-color: #edf8b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col10 {\n",
       "  background-color: #fbfdcf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row1_col11 {\n",
       "  background-color: #f1fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col0 {\n",
       "  background-color: #69bf72;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col1, #T_c5c88_row2_col5, #T_c5c88_row5_col11 {\n",
       "  background-color: #97d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col2 {\n",
       "  background-color: #88cd7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col3, #T_c5c88_row12_col9 {\n",
       "  background-color: #90d083;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col4, #T_c5c88_row15_col2 {\n",
       "  background-color: #86cc7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col6, #T_c5c88_row23_col2 {\n",
       "  background-color: #f5fbb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col9 {\n",
       "  background-color: #f8fcbe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row2_col11 {\n",
       "  background-color: #f8fdc1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col0, #T_c5c88_row10_col2 {\n",
       "  background-color: #a6da8b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col1, #T_c5c88_row20_col0 {\n",
       "  background-color: #bce395;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col2, #T_c5c88_row20_col4 {\n",
       "  background-color: #9fd788;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col3 {\n",
       "  background-color: #cbea9c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col4, #T_c5c88_row20_col6 {\n",
       "  background-color: #9dd688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col5, #T_c5c88_row23_col3 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col6 {\n",
       "  background-color: #006536;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row5_col7 {\n",
       "  background-color: #005d33;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row5_col8, #T_c5c88_row12_col0, #T_c5c88_row12_col5, #T_c5c88_row12_col7, #T_c5c88_row14_col1, #T_c5c88_row14_col2, #T_c5c88_row14_col3, #T_c5c88_row14_col4, #T_c5c88_row14_col6, #T_c5c88_row14_col9, #T_c5c88_row14_col10, #T_c5c88_row14_col11, #T_c5c88_row14_col12 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row5_col9 {\n",
       "  background-color: #bae394;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row5_col10 {\n",
       "  background-color: #278946;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row5_col12 {\n",
       "  background-color: #12763d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row7_col0, #T_c5c88_row7_col5 {\n",
       "  background-color: #ebf7b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col1, #T_c5c88_row23_col5 {\n",
       "  background-color: #f7fcb9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col2 {\n",
       "  background-color: #fcfed3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col3, #T_c5c88_row23_col4 {\n",
       "  background-color: #f8fcbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col4 {\n",
       "  background-color: #fcfed7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col8 {\n",
       "  background-color: #ddf2a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col9, #T_c5c88_row20_col9 {\n",
       "  background-color: #fdfed9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col10, #T_c5c88_row19_col9 {\n",
       "  background-color: #feffdf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col11, #T_c5c88_row21_col9 {\n",
       "  background-color: #fdfedd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row7_col12 {\n",
       "  background-color: #fbfed2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row10_col0 {\n",
       "  background-color: #a2d88a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row10_col1 {\n",
       "  background-color: #339951;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row10_col3 {\n",
       "  background-color: #006837;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row10_col4 {\n",
       "  background-color: #aedd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row12_col1 {\n",
       "  background-color: #3aa257;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row12_col2 {\n",
       "  background-color: #00542f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row12_col3 {\n",
       "  background-color: #329850;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row12_col4 {\n",
       "  background-color: #00532f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row12_col6 {\n",
       "  background-color: #8bce81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row12_col10 {\n",
       "  background-color: #edf8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row12_col11 {\n",
       "  background-color: #b3e091;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row14_col0 {\n",
       "  background-color: #10743c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row14_col5 {\n",
       "  background-color: #3ca458;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row14_col7 {\n",
       "  background-color: #cfec9e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row14_col8 {\n",
       "  background-color: #6bc072;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col0 {\n",
       "  background-color: #77c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col1 {\n",
       "  background-color: #5db96b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row15_col3 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c5c88_row15_col4 {\n",
       "  background-color: #89ce80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col5 {\n",
       "  background-color: #92d183;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col9 {\n",
       "  background-color: #f6fcb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col10 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col11 {\n",
       "  background-color: #f9fdc5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row15_col12, #T_c5c88_row20_col11, #T_c5c88_row23_col12 {\n",
       "  background-color: #feffe2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row16_col7 {\n",
       "  background-color: #c9e99c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row19_col1, #T_c5c88_row20_col12 {\n",
       "  background-color: #ffffe4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row19_col3 {\n",
       "  background-color: #fbfdce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row20_col1 {\n",
       "  background-color: #e0f3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row20_col2 {\n",
       "  background-color: #a4d98a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row20_col3, #T_c5c88_row23_col1 {\n",
       "  background-color: #d0ec9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row20_col5 {\n",
       "  background-color: #a7db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row20_col7 {\n",
       "  background-color: #7cc87b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row23_col0, #T_c5c88_row23_col11 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c5c88_row23_col9 {\n",
       "  background-color: #e5f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c5c88\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c5c88_level0_col0\" class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th id=\"T_c5c88_level0_col1\" class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th id=\"T_c5c88_level0_col2\" class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th id=\"T_c5c88_level0_col3\" class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th id=\"T_c5c88_level0_col4\" class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th id=\"T_c5c88_level0_col5\" class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th id=\"T_c5c88_level0_col6\" class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th id=\"T_c5c88_level0_col7\" class=\"col_heading level0 col7\" >WR</th>\n",
       "      <th id=\"T_c5c88_level0_col8\" class=\"col_heading level0 col8\" >MDD</th>\n",
       "      <th id=\"T_c5c88_level0_col9\" class=\"col_heading level0 col9\" >ADD</th>\n",
       "      <th id=\"T_c5c88_level0_col10\" class=\"col_heading level0 col10\" >CDaR</th>\n",
       "      <th id=\"T_c5c88_level0_col11\" class=\"col_heading level0 col11\" >UCI</th>\n",
       "      <th id=\"T_c5c88_level0_col12\" class=\"col_heading level0 col12\" >EDaR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_c5c88_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_c5c88_row1_col0\" class=\"data row1 col0\" >6.16%</td>\n",
       "      <td id=\"T_c5c88_row1_col1\" class=\"data row1 col1\" >7.63%</td>\n",
       "      <td id=\"T_c5c88_row1_col2\" class=\"data row1 col2\" >4.38%</td>\n",
       "      <td id=\"T_c5c88_row1_col3\" class=\"data row1 col3\" >6.11%</td>\n",
       "      <td id=\"T_c5c88_row1_col4\" class=\"data row1 col4\" >3.98%</td>\n",
       "      <td id=\"T_c5c88_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row1_col6\" class=\"data row1 col6\" >1.58%</td>\n",
       "      <td id=\"T_c5c88_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row1_col8\" class=\"data row1 col8\" >1.20%</td>\n",
       "      <td id=\"T_c5c88_row1_col9\" class=\"data row1 col9\" >6.79%</td>\n",
       "      <td id=\"T_c5c88_row1_col10\" class=\"data row1 col10\" >2.81%</td>\n",
       "      <td id=\"T_c5c88_row1_col11\" class=\"data row1 col11\" >6.34%</td>\n",
       "      <td id=\"T_c5c88_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_c5c88_row2_col0\" class=\"data row2 col0\" >11.50%</td>\n",
       "      <td id=\"T_c5c88_row2_col1\" class=\"data row2 col1\" >9.24%</td>\n",
       "      <td id=\"T_c5c88_row2_col2\" class=\"data row2 col2\" >10.37%</td>\n",
       "      <td id=\"T_c5c88_row2_col3\" class=\"data row2 col3\" >9.04%</td>\n",
       "      <td id=\"T_c5c88_row2_col4\" class=\"data row2 col4\" >10.62%</td>\n",
       "      <td id=\"T_c5c88_row2_col5\" class=\"data row2 col5\" >12.35%</td>\n",
       "      <td id=\"T_c5c88_row2_col6\" class=\"data row2 col6\" >4.62%</td>\n",
       "      <td id=\"T_c5c88_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row2_col9\" class=\"data row2 col9\" >4.48%</td>\n",
       "      <td id=\"T_c5c88_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row2_col11\" class=\"data row2 col11\" >4.34%</td>\n",
       "      <td id=\"T_c5c88_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_c5c88_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_c5c88_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_c5c88_row5_col0\" class=\"data row5 col0\" >8.48%</td>\n",
       "      <td id=\"T_c5c88_row5_col1\" class=\"data row5 col1\" >7.21%</td>\n",
       "      <td id=\"T_c5c88_row5_col2\" class=\"data row5 col2\" >9.15%</td>\n",
       "      <td id=\"T_c5c88_row5_col3\" class=\"data row5 col3\" >5.96%</td>\n",
       "      <td id=\"T_c5c88_row5_col4\" class=\"data row5 col4\" >9.38%</td>\n",
       "      <td id=\"T_c5c88_row5_col5\" class=\"data row5 col5\" >7.49%</td>\n",
       "      <td id=\"T_c5c88_row5_col6\" class=\"data row5 col6\" >30.14%</td>\n",
       "      <td id=\"T_c5c88_row5_col7\" class=\"data row5 col7\" >28.96%</td>\n",
       "      <td id=\"T_c5c88_row5_col8\" class=\"data row5 col8\" >56.01%</td>\n",
       "      <td id=\"T_c5c88_row5_col9\" class=\"data row5 col9\" >13.49%</td>\n",
       "      <td id=\"T_c5c88_row5_col10\" class=\"data row5 col10\" >32.93%</td>\n",
       "      <td id=\"T_c5c88_row5_col11\" class=\"data row5 col11\" >18.16%</td>\n",
       "      <td id=\"T_c5c88_row5_col12\" class=\"data row5 col12\" >42.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_c5c88_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col7\" class=\"data row6 col7\" >5.37%</td>\n",
       "      <td id=\"T_c5c88_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_c5c88_row7_col0\" class=\"data row7 col0\" >3.82%</td>\n",
       "      <td id=\"T_c5c88_row7_col1\" class=\"data row7 col1\" >2.73%</td>\n",
       "      <td id=\"T_c5c88_row7_col2\" class=\"data row7 col2\" >1.18%</td>\n",
       "      <td id=\"T_c5c88_row7_col3\" class=\"data row7 col3\" >2.33%</td>\n",
       "      <td id=\"T_c5c88_row7_col4\" class=\"data row7 col4\" >0.90%</td>\n",
       "      <td id=\"T_c5c88_row7_col5\" class=\"data row7 col5\" >5.14%</td>\n",
       "      <td id=\"T_c5c88_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row7_col8\" class=\"data row7 col8\" >13.08%</td>\n",
       "      <td id=\"T_c5c88_row7_col9\" class=\"data row7 col9\" >1.49%</td>\n",
       "      <td id=\"T_c5c88_row7_col10\" class=\"data row7 col10\" >0.82%</td>\n",
       "      <td id=\"T_c5c88_row7_col11\" class=\"data row7 col11\" >1.01%</td>\n",
       "      <td id=\"T_c5c88_row7_col12\" class=\"data row7 col12\" >2.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_c5c88_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_c5c88_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_c5c88_row10_col0\" class=\"data row10 col0\" >8.63%</td>\n",
       "      <td id=\"T_c5c88_row10_col1\" class=\"data row10 col1\" >14.71%</td>\n",
       "      <td id=\"T_c5c88_row10_col2\" class=\"data row10 col2\" >8.82%</td>\n",
       "      <td id=\"T_c5c88_row10_col3\" class=\"data row10 col3\" >17.80%</td>\n",
       "      <td id=\"T_c5c88_row10_col4\" class=\"data row10 col4\" >8.46%</td>\n",
       "      <td id=\"T_c5c88_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_c5c88_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_c5c88_row12_col0\" class=\"data row12 col0\" >21.54%</td>\n",
       "      <td id=\"T_c5c88_row12_col1\" class=\"data row12 col1\" >14.12%</td>\n",
       "      <td id=\"T_c5c88_row12_col2\" class=\"data row12 col2\" >21.24%</td>\n",
       "      <td id=\"T_c5c88_row12_col3\" class=\"data row12 col3\" >13.93%</td>\n",
       "      <td id=\"T_c5c88_row12_col4\" class=\"data row12 col4\" >21.58%</td>\n",
       "      <td id=\"T_c5c88_row12_col5\" class=\"data row12 col5\" >28.82%</td>\n",
       "      <td id=\"T_c5c88_row12_col6\" class=\"data row12 col6\" >15.55%</td>\n",
       "      <td id=\"T_c5c88_row12_col7\" class=\"data row12 col7\" >31.77%</td>\n",
       "      <td id=\"T_c5c88_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row12_col9\" class=\"data row12 col9\" >17.66%</td>\n",
       "      <td id=\"T_c5c88_row12_col10\" class=\"data row12 col10\" >7.46%</td>\n",
       "      <td id=\"T_c5c88_row12_col11\" class=\"data row12 col11\" >15.14%</td>\n",
       "      <td id=\"T_c5c88_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_c5c88_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_c5c88_row14_col0\" class=\"data row14 col0\" >17.59%</td>\n",
       "      <td id=\"T_c5c88_row14_col1\" class=\"data row14 col1\" >21.53%</td>\n",
       "      <td id=\"T_c5c88_row14_col2\" class=\"data row14 col2\" >22.47%</td>\n",
       "      <td id=\"T_c5c88_row14_col3\" class=\"data row14 col3\" >20.36%</td>\n",
       "      <td id=\"T_c5c88_row14_col4\" class=\"data row14 col4\" >22.75%</td>\n",
       "      <td id=\"T_c5c88_row14_col5\" class=\"data row14 col5\" >18.64%</td>\n",
       "      <td id=\"T_c5c88_row14_col6\" class=\"data row14 col6\" >34.11%</td>\n",
       "      <td id=\"T_c5c88_row14_col7\" class=\"data row14 col7\" >8.89%</td>\n",
       "      <td id=\"T_c5c88_row14_col8\" class=\"data row14 col8\" >29.71%</td>\n",
       "      <td id=\"T_c5c88_row14_col9\" class=\"data row14 col9\" >39.71%</td>\n",
       "      <td id=\"T_c5c88_row14_col10\" class=\"data row14 col10\" >44.96%</td>\n",
       "      <td id=\"T_c5c88_row14_col11\" class=\"data row14 col11\" >42.31%</td>\n",
       "      <td id=\"T_c5c88_row14_col12\" class=\"data row14 col12\" >52.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_c5c88_row15_col0\" class=\"data row15 col0\" >10.83%</td>\n",
       "      <td id=\"T_c5c88_row15_col1\" class=\"data row15 col1\" >12.07%</td>\n",
       "      <td id=\"T_c5c88_row15_col2\" class=\"data row15 col2\" >10.47%</td>\n",
       "      <td id=\"T_c5c88_row15_col3\" class=\"data row15 col3\" >12.23%</td>\n",
       "      <td id=\"T_c5c88_row15_col4\" class=\"data row15 col4\" >10.43%</td>\n",
       "      <td id=\"T_c5c88_row15_col5\" class=\"data row15 col5\" >12.69%</td>\n",
       "      <td id=\"T_c5c88_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row15_col9\" class=\"data row15 col9\" >5.16%</td>\n",
       "      <td id=\"T_c5c88_row15_col10\" class=\"data row15 col10\" >3.35%</td>\n",
       "      <td id=\"T_c5c88_row15_col11\" class=\"data row15 col11\" >3.89%</td>\n",
       "      <td id=\"T_c5c88_row15_col12\" class=\"data row15 col12\" >0.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_c5c88_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col7\" class=\"data row16 col7\" >9.39%</td>\n",
       "      <td id=\"T_c5c88_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_c5c88_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_c5c88_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_c5c88_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col1\" class=\"data row19 col1\" >0.13%</td>\n",
       "      <td id=\"T_c5c88_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col3\" class=\"data row19 col3\" >1.36%</td>\n",
       "      <td id=\"T_c5c88_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col9\" class=\"data row19 col9\" >0.66%</td>\n",
       "      <td id=\"T_c5c88_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_c5c88_row20_col0\" class=\"data row20 col0\" >7.18%</td>\n",
       "      <td id=\"T_c5c88_row20_col1\" class=\"data row20 col1\" >4.73%</td>\n",
       "      <td id=\"T_c5c88_row20_col2\" class=\"data row20 col2\" >8.91%</td>\n",
       "      <td id=\"T_c5c88_row20_col3\" class=\"data row20 col3\" >5.60%</td>\n",
       "      <td id=\"T_c5c88_row20_col4\" class=\"data row20 col4\" >9.26%</td>\n",
       "      <td id=\"T_c5c88_row20_col5\" class=\"data row20 col5\" >11.20%</td>\n",
       "      <td id=\"T_c5c88_row20_col6\" class=\"data row20 col6\" >14.00%</td>\n",
       "      <td id=\"T_c5c88_row20_col7\" class=\"data row20 col7\" >15.63%</td>\n",
       "      <td id=\"T_c5c88_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row20_col9\" class=\"data row20 col9\" >1.49%</td>\n",
       "      <td id=\"T_c5c88_row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row20_col11\" class=\"data row20 col11\" >0.49%</td>\n",
       "      <td id=\"T_c5c88_row20_col12\" class=\"data row20 col12\" >0.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_c5c88_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col9\" class=\"data row21 col9\" >1.07%</td>\n",
       "      <td id=\"T_c5c88_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_c5c88_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_c5c88_row23_col0\" class=\"data row23 col0\" >4.27%</td>\n",
       "      <td id=\"T_c5c88_row23_col1\" class=\"data row23 col1\" >5.90%</td>\n",
       "      <td id=\"T_c5c88_row23_col2\" class=\"data row23 col2\" >3.00%</td>\n",
       "      <td id=\"T_c5c88_row23_col3\" class=\"data row23 col3\" >5.28%</td>\n",
       "      <td id=\"T_c5c88_row23_col4\" class=\"data row23 col4\" >2.63%</td>\n",
       "      <td id=\"T_c5c88_row23_col5\" class=\"data row23 col5\" >3.68%</td>\n",
       "      <td id=\"T_c5c88_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row23_col9\" class=\"data row23 col9\" >8.00%</td>\n",
       "      <td id=\"T_c5c88_row23_col10\" class=\"data row23 col10\" >7.65%</td>\n",
       "      <td id=\"T_c5c88_row23_col11\" class=\"data row23 col11\" >8.32%</td>\n",
       "      <td id=\"T_c5c88_row23_col12\" class=\"data row23 col12\" >0.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c5c88_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_c5c88_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "      <td id=\"T_c5c88_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x316ea5e50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "6e72041bc49a6ff39e74ccd56b9391c544b799a0d2a04160b530f8b1ce965df8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
